Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "46" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 44 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 42 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459859 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.896702 | 11.204995 | -0.324013 | 9.647264 | 0.217824 | 2.762108 | 0.238196 | 1.490004 | 0.7218 | 0.0382 | 0.5629 | nan | nan |
| 2459858 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.845228 | 11.904860 | -0.371043 | 9.885481 | 1.147652 | 2.842872 | 0.358188 | 2.986148 | 0.7324 | 0.0368 | 0.5627 | 2.857332 | 1.144282 |
| 2459857 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.077931 | 6.409785 | -0.088711 | 1.419573 | 0.449217 | 4.451016 | -0.824568 | 10.292775 | 0.0250 | 0.0239 | 0.0009 | nan | nan |
| 2459856 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.202236 | 18.072974 | -0.655102 | 24.345223 | -0.911342 | 12.072762 | 0.164048 | 4.173755 | 0.7251 | 0.0382 | 0.5570 | 3.123524 | 1.201149 |
| 2459855 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.138634 | 17.636350 | -1.019219 | 25.145012 | -0.271961 | 4.266429 | -0.111170 | 1.652898 | 0.6995 | 0.0379 | 0.5304 | 3.333062 | 1.202419 |
| 2459854 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.365057 | 15.943695 | 0.148350 | 19.201966 | -0.181286 | 4.290313 | 0.574660 | 4.508095 | 0.7273 | 0.0399 | 0.5647 | 3.186843 | 1.191723 |
| 2459853 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.266896 | 15.771808 | 0.793469 | 26.629936 | 0.229348 | 10.824928 | 0.279276 | 4.474593 | 0.7451 | 0.0396 | 0.5794 | 3.054161 | 1.198064 |
| 2459852 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.219745 | 15.761808 | 1.448143 | 27.747721 | 1.353071 | 20.008869 | 0.969797 | 16.551594 | 0.8381 | 0.0402 | 0.5545 | 5.034280 | 1.203312 |
| 2459851 | digital_ok | 100.00% | 0.00% | 90.23% | 0.00% | 100.00% | 0.00% | -0.870982 | 21.129022 | 1.192797 | 29.780782 | 4.611658 | 43.523799 | 4.060301 | 21.892980 | 0.7581 | 0.0868 | 0.5551 | 2.821282 | 1.102008 |
| 2459850 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.225110 | 18.734903 | 0.711988 | 24.792202 | 0.471514 | 19.809234 | 1.880198 | 17.586417 | 0.0548 | 0.0266 | 0.0271 | 1.192065 | 1.162291 |
| 2459849 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.262661 | 17.190111 | 2.304733 | 49.067601 | 1.390340 | 12.847575 | 0.733731 | 7.506802 | 0.0569 | 0.0268 | 0.0294 | 1.232177 | 1.194819 |
| 2459848 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.236915 | 15.210787 | 2.204795 | 31.820874 | 1.162715 | 21.791425 | -0.151994 | 4.610982 | 0.0588 | 0.0265 | 0.0292 | 1.218008 | 1.184295 |
| 2459847 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.129775 | 17.659107 | 2.545284 | 29.996529 | 0.295472 | 28.460301 | 0.041629 | 1.790526 | 0.0469 | 0.0258 | 0.0168 | 1.211197 | 1.181267 |
| 2459846 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.712469 | 26.363011 | 2.032376 | 35.009523 | 2.013114 | 20.882197 | -0.302327 | 5.104684 | 0.0591 | 0.0268 | 0.0350 | 1.193450 | 1.153493 |
| 2459845 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.888199 | 19.570064 | 2.924736 | 41.149453 | 0.211475 | 16.516572 | -0.075566 | 2.004190 | 0.7557 | 0.0470 | 0.6182 | 8.207091 | 1.245983 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 2.309659 | 15.443402 | 0.568554 | 6.913147 | 1.018883 | 4.913375 | -0.654799 | 13.450994 | 0.0247 | 0.0238 | 0.0009 | nan | nan |
| 2459843 | digital_ok | 100.00% | 0.66% | 100.00% | 0.00% | 100.00% | 0.00% | -1.563119 | 19.135496 | -0.149899 | 20.259934 | 0.087452 | 71.034518 | 0.203346 | 0.814512 | 0.7553 | 0.0394 | 0.5959 | 4.436371 | 1.288283 |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.876741 | -0.493142 | -0.538075 | -0.902363 | -0.615874 | 0.731730 | -0.895027 | 0.997842 | 0.0233 | 0.0239 | 0.0009 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | -0.955873 | -0.781584 | -1.185916 | -1.325418 | -1.176652 | -0.790419 | -1.439898 | 0.650643 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.61% | 0.00% | -1.276581 | 0.088040 | 0.454769 | -0.762581 | 2.203311 | 0.032051 | -0.547845 | 0.028656 | 0.7152 | 0.6672 | 0.4358 | 1.339856 | 1.292584 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0306 | 0.0321 | 0.0009 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.313467 | -0.428325 | 2.516889 | 0.342344 | 5.007060 | 4.531111 | 14.333239 | 12.636050 | 0.0303 | 0.0321 | 0.0004 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 2.814239 | 1.002133 | 1.193880 | 0.521403 | 3.872214 | 2.125633 | 1.535357 | 1.933363 | 0.0261 | 0.0282 | 0.0029 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.63% | -1.703101 | -0.893510 | 0.230529 | -0.874099 | 0.782583 | -0.738320 | 0.409430 | 1.586476 | 0.8130 | 0.4923 | 0.6171 | 1.680040 | 1.400917 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.683280 | 1.481289 | -1.223331 | -0.492849 | -1.289860 | -0.257010 | -0.959564 | 0.373274 | 0.0252 | 0.0314 | 0.0040 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.401906 | -0.678747 | 0.032536 | -0.702498 | 0.328524 | -0.080738 | -0.046774 | 2.756697 | 0.8014 | 0.4777 | 0.6195 | 1.102546 | 0.847903 |
| 2459829 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 92.16% | 7.19% | -1.510065 | -0.108924 | -0.572812 | -0.673325 | 1.337229 | -0.698660 | 0.693861 | 0.704559 | 0.7330 | 0.6243 | 0.4574 | -0.000000 | -0.000000 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.086278 | -0.261767 | -0.309252 | -0.413490 | 0.098044 | 0.300081 | 0.680272 | 6.783467 | 0.7976 | 0.4971 | 0.5939 | 2.802278 | 2.032128 |
| 2459827 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.914365 | 0.398593 | 0.233326 | -0.782904 | -0.778107 | -0.777720 | -0.211314 | 4.571148 | 0.7417 | 0.6323 | 0.4611 | 5.554254 | 5.356632 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.63% | -1.082649 | -0.721218 | 0.896764 | -0.735209 | -0.491848 | -1.105421 | -0.010786 | -0.309709 | 0.7914 | 0.5164 | 0.5667 | 0.786214 | 0.523758 |
| 2459825 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.217380 | -0.917545 | 0.353717 | -0.531880 | -1.166669 | -0.889093 | -0.333100 | -0.242950 | 0.7891 | 0.5142 | 0.5767 | 1.335047 | 1.037340 |
| 2459824 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.147826 | -0.021683 | 0.350038 | -0.829884 | -0.357787 | -0.324386 | -0.154327 | 1.250523 | 0.6686 | 0.6724 | 0.4241 | -0.007247 | -0.007905 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.078677 | -0.858797 | 0.082972 | -0.542656 | -0.305754 | -0.600263 | -0.115259 | 14.841478 | 0.7318 | 0.5813 | 0.5219 | 3.143487 | 3.045796 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.940806 | -0.900503 | 0.429026 | -0.734618 | -0.887032 | -0.740958 | -0.523008 | 0.265657 | 0.7876 | 0.5413 | 0.5701 | 1.477637 | 1.290423 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.884027 | -0.222600 | -0.073356 | -0.949405 | -0.931486 | -1.267051 | 0.462361 | -1.414038 | 0.7851 | 0.5644 | 0.5708 | 1.478142 | 1.409617 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.53% | -1.005801 | 0.013202 | -0.280187 | -0.779893 | 0.634802 | -1.441358 | 0.283762 | 2.259659 | 0.7597 | 0.6666 | 0.4432 | 1.545798 | 1.422689 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.118826 | -0.606904 | -0.177138 | -0.859716 | -1.074353 | -1.763067 | -0.602690 | 1.162018 | 0.8079 | 0.6381 | 0.5397 | 1.768016 | 1.589170 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.018579 | 0.116721 | 0.405575 | -0.759875 | -0.272423 | -0.687698 | -0.420665 | 1.931068 | 0.8446 | 0.5732 | 0.6164 | 1.569837 | 1.417207 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.913187 | -0.471808 | 0.591265 | -0.918381 | -1.107928 | -0.480856 | -0.690895 | 3.315403 | 0.7974 | 0.6417 | 0.5507 | 1.854552 | 1.612947 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Shape | 11.204995 | -0.896702 | 11.204995 | -0.324013 | 9.647264 | 0.217824 | 2.762108 | 0.238196 | 1.490004 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Shape | 11.904860 | 11.904860 | -0.845228 | 9.885481 | -0.371043 | 2.842872 | 1.147652 | 2.986148 | 0.358188 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 10.292775 | 6.409785 | 0.077931 | 1.419573 | -0.088711 | 4.451016 | 0.449217 | 10.292775 | -0.824568 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 24.345223 | -1.202236 | 18.072974 | -0.655102 | 24.345223 | -0.911342 | 12.072762 | 0.164048 | 4.173755 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 25.145012 | 17.636350 | -1.138634 | 25.145012 | -1.019219 | 4.266429 | -0.271961 | 1.652898 | -0.111170 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 19.201966 | 15.943695 | -1.365057 | 19.201966 | 0.148350 | 4.290313 | -0.181286 | 4.508095 | 0.574660 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 26.629936 | 15.771808 | -1.266896 | 26.629936 | 0.793469 | 10.824928 | 0.229348 | 4.474593 | 0.279276 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 27.747721 | -1.219745 | 15.761808 | 1.448143 | 27.747721 | 1.353071 | 20.008869 | 0.969797 | 16.551594 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Variability | 43.523799 | -0.870982 | 21.129022 | 1.192797 | 29.780782 | 4.611658 | 43.523799 | 4.060301 | 21.892980 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 24.792202 | -1.225110 | 18.734903 | 0.711988 | 24.792202 | 0.471514 | 19.809234 | 1.880198 | 17.586417 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 49.067601 | -1.262661 | 17.190111 | 2.304733 | 49.067601 | 1.390340 | 12.847575 | 0.733731 | 7.506802 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 31.820874 | 15.210787 | -1.236915 | 31.820874 | 2.204795 | 21.791425 | 1.162715 | 4.610982 | -0.151994 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 29.996529 | 17.659107 | -1.129775 | 29.996529 | 2.545284 | 28.460301 | 0.295472 | 1.790526 | 0.041629 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 35.009523 | -1.712469 | 26.363011 | 2.032376 | 35.009523 | 2.013114 | 20.882197 | -0.302327 | 5.104684 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Power | 41.149453 | 19.570064 | -0.888199 | 41.149453 | 2.924736 | 16.516572 | 0.211475 | 2.004190 | -0.075566 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Shape | 15.443402 | 2.309659 | 15.443402 | 0.568554 | 6.913147 | 1.018883 | 4.913375 | -0.654799 | 13.450994 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Variability | 71.034518 | 19.135496 | -1.563119 | 20.259934 | -0.149899 | 71.034518 | 0.087452 | 0.814512 | 0.203346 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 0.997842 | -0.876741 | -0.493142 | -0.538075 | -0.902363 | -0.615874 | 0.731730 | -0.895027 | 0.997842 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 0.650643 | -0.781584 | -0.955873 | -1.325418 | -1.185916 | -0.790419 | -1.176652 | 0.650643 | -1.439898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Temporal Variability | 2.203311 | 0.088040 | -1.276581 | -0.762581 | 0.454769 | 0.032051 | 2.203311 | 0.028656 | -0.547845 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Temporal Discontinuties | 14.333239 | -0.428325 | 0.313467 | 0.342344 | 2.516889 | 4.531111 | 5.007060 | 12.636050 | 14.333239 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Temporal Variability | 3.872214 | 1.002133 | 2.814239 | 0.521403 | 1.193880 | 2.125633 | 3.872214 | 1.933363 | 1.535357 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 1.586476 | -1.703101 | -0.893510 | 0.230529 | -0.874099 | 0.782583 | -0.738320 | 0.409430 | 1.586476 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Shape | 1.481289 | -0.683280 | 1.481289 | -1.223331 | -0.492849 | -1.289860 | -0.257010 | -0.959564 | 0.373274 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 2.756697 | -1.401906 | -0.678747 | 0.032536 | -0.702498 | 0.328524 | -0.080738 | -0.046774 | 2.756697 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Temporal Variability | 1.337229 | -0.108924 | -1.510065 | -0.673325 | -0.572812 | -0.698660 | 1.337229 | 0.704559 | 0.693861 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 6.783467 | -0.261767 | -1.086278 | -0.413490 | -0.309252 | 0.300081 | 0.098044 | 6.783467 | 0.680272 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 4.571148 | -0.914365 | 0.398593 | 0.233326 | -0.782904 | -0.778107 | -0.777720 | -0.211314 | 4.571148 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Power | 0.896764 | -0.721218 | -1.082649 | -0.735209 | 0.896764 | -1.105421 | -0.491848 | -0.309709 | -0.010786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Power | 0.353717 | -0.917545 | -1.217380 | -0.531880 | 0.353717 | -0.889093 | -1.166669 | -0.242950 | -0.333100 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 1.250523 | -1.147826 | -0.021683 | 0.350038 | -0.829884 | -0.357787 | -0.324386 | -0.154327 | 1.250523 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 14.841478 | -0.858797 | -1.078677 | -0.542656 | 0.082972 | -0.600263 | -0.305754 | 14.841478 | -0.115259 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Power | 0.429026 | -0.940806 | -0.900503 | 0.429026 | -0.734618 | -0.887032 | -0.740958 | -0.523008 | 0.265657 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | ee Temporal Discontinuties | 0.462361 | -0.222600 | -0.884027 | -0.949405 | -0.073356 | -1.267051 | -0.931486 | -1.414038 | 0.462361 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 2.259659 | -1.005801 | 0.013202 | -0.280187 | -0.779893 | 0.634802 | -1.441358 | 0.283762 | 2.259659 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 1.162018 | -1.118826 | -0.606904 | -0.177138 | -0.859716 | -1.074353 | -1.763067 | -0.602690 | 1.162018 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 1.931068 | 0.116721 | -1.018579 | -0.759875 | 0.405575 | -0.687698 | -0.272423 | 1.931068 | -0.420665 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Temporal Discontinuties | 3.315403 | -0.471808 | -0.913187 | -0.918381 | 0.591265 | -0.480856 | -1.107928 | 3.315403 | -0.690895 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | N05 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |